Impact of Patient Communication Style on Agentic AI-Generated Clinical Advice in E-Medicine.

ABSTRACT:

Background

E-medicine use has surged, and health systems are exploring LLMs for message triage. However, it is still unknown whether patient tone alone alters AI-generated clinical or administrative decisions.

Methods

We created 1,000 clinician-validated primary-care vignettes (500 clinical, 500 sick-leave) and presented each in eight communication styles. Five agentic LLMs generated structured outputs for triage urgency, sick-leave decisions and other outputs.Differences from the neutral control were assessed using chi-square tests (Cramér's V) and t-tests (Cohen's d), with FDR correction. As external validation, 40 real patient e-messages from a large health network were processed using the same pipeline.

Results

Across 120,000 agent runs, patient tone produc

SUBMITTER: Omar M 

PROVIDER: S-EPMC12822792 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature